Visual content recognition and understanding have greatly made progress based on the advances of deep learning methods that construct the discriminative model by training large-scale labeled data. In fact, two reasons limit the current deep learning methods for efficiently learning new categories. One is that human annotation cost is high for large-scale data (for example, thousands of the diversity samples in the same category and hundreds of the various categories in one cognition domain), the other is that the rare samples of some categories are not enough for the discriminative model training. Therefore, it is still a challenge question that the discriminative model is learned from the rare samples of the categories. To solve this question, few-shot learning [1]} [2]} [3]} [4]} [5]} [6]} [7]} [8]} [9]} [10]} [11]} [12]}proposed from the inspiration of human visual system has been an attracted research to generalize the learning model to new classes with the rare samples of each novel category by feature learning [13]} [14]} [15]} [16]} [17]} [18]}or meta-learning [12]} [20]} [21]}[22]} [23]} [24]}. Feature learning emphasises on feature generation and extraction model construction based on invariance transfer information, while meta-learning focuses on the relevance model between the samples for mining the common relationship of data samples by the episode training.
